MRI in the diagnosis of pediatric multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: MRI diagnostic criteria have not yet been adopted for pediatric multiple sclerosis (MS). MRI plays a pivotal role in supporting the diagnosis of MS in adults. We sought to quantitatively define the MRI features of pediatric MS, to determine features that distinguish MS from nondemyelinating relapsing childhood neurologic disorders, and to propose MRI criteria for lesion dissemination in space in children with MS. METHODS: A retrospective analysis of MRI scans from 38 children with clinically definite MS and 45 children with nondemyelinating diseases with relapsing neurologic deficits (migraine, systemic lupus erythematosus) was performed. For each scan, T2/FLAIR hyperintense lesions were quantified and categorized according to location and size. Mean lesion counts in specific locations were compared between groups to derive diagnostic criteria. Validation of the proposed criteria was performed using MRI scans from a second independent MS cohort (n = 21). RESULTS: MRI lesion location and size categories differed between children with MS and nondemyelinating controls with a medium to large effect size for most variables. The presence of at least two of the following-five or more lesions, two or more periventricular lesions, or one brainstem lesion-distinguished MS from other nondemyelinating disease controls with 85% sensitivity and 98% specificity. CONCLUSIONS: We propose modifications to the currently established McDonald MRI criteria for lesion dissemination in space that will enhance the diagnostic accuracy of these criteria for multiple sclerosis in children.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".